import re

# 假设你的文本存储在一个长字符串变量text中
text = """
[1]	LIU L, OUYANG W, WANG X, et al. Deep learning for generic object detection: A survey[J/OL]. International journal of computer vision, 2020, 128(2): 261-318. http://dx.doi.org/10.1007/s11263-019-01247-4. DOI:10.1007/s11263-019-01247-4.
[2]	ZOU Z, CHEN K, SHI Z, et al. Object detection in 20 years: A survey[J/OL]. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2023, 111(3): 257-276. http://dx.doi.org/10.1109/JPROC.2023.3238524. DOI:10.1109/jproc.2023.3238524.
[3]	OÑORO-RUBIO D, LÓPEZ-SASTRE R J. Towards perspective-free object counting with deep learning[M/OL]//Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 615-629. http://dx.doi.org/10.1007/978-3-319-46478-7_38. DOI:10.1007/978-3-319-46478-7_38.
[4]	HSIEH M R, LIN Y L, HSU W H. Drone-based object counting by spatially regularized regional proposal network[C/OL]//2017 IEEE International Conference on Computer Vision (ICCV), 2017/10/22-2017/10/29, Venice. IEEE, 2017. https://doi.org/10.1109/iccv.2017.446. DOI:10.1109/iccv.2017.446.
[5]	CHOLAKKAL H, SUN G, SHAHBAZ KHAN F, et al. Object counting and instance segmentation with image-level supervision[C/OL]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019/6/15-2019/6/20, Long Beach, CA, USA. IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01268. DOI:10.1109/cvpr.2019.01268.
[6]	DJUKIC N, LUKEZIC A, ZAVRTANIK V, et al. A low-shot Object Counting network with iterative prototype Adaptation[Z/OL]//arXiv [cs.CV]. (2022-11-15). https://doi.org/10.48550/arXiv.2211.08217. DOI:10.48550/ARXIV.2211.08217.
[7]	CHENG Z Q, DAI Q, LI H, et al. Rethinking spatial invariance of convolutional networks for object counting[Z/OL]//arXiv [cs.CV]. (2022-06-10). https://doi.org/10.48550/arXiv.2206.05253. DOI:10.48550/ARXIV.2206.05253.
[8]	JIANG R, LIU L, CHEN C. CLIP-Count: Towards text-guided zero-shot object counting[Z/OL]//arXiv [cs.CV]. (2023-05-12). https://doi.org/10.48550/arXiv.2305.07304. DOI:10.48550/ARXIV.2305.07304.
[9]	KANG S, MOON W, KIM E, et al. VLCounter: Text-aware VIsual representation for zero-Shot Object Counting[Z/OL]//arXiv [cs.CV]. 2023: 2714-2722. https://doi.org/10.1609/aaai.v38i3.28050. DOI:10.1609/aaai.v38i3.28050.
[10]	AMINI-NAIENI N, AMINI-NAIENI K, HAN T, et al. Open-world text-specified object counting[Z/OL]//arXiv [cs.CV]. (2023-06-02). https://doi.org/10.48550/arxiv.2306.01851. DOI:10.48550/ARXIV.2306.01851.
[11]	XU J, LE H, NGUYEN V, et al. Zero-shot object counting[Z/OL]//arXiv [cs.CV]. (2023-03-03). https://doi.org/10.48550/arXiv.2303.02001. DOI:10.48550/ARXIV.2303.02001.
[12]	AMINI-NAIENI N, HAN T, ZISSERMAN A. CountGD: Multi-Modal Open-World Counting[Z/OL]//arXiv [cs.CV]. (2024-07-05). https://doi.org/10.48550/arxiv.2407.04619. DOI:10.48550/ARXIV.2407.04619.
[13]	ZHANG Y, ZHOU D, CHEN S, et al. Single-image crowd counting via multi-column convolutional neural network[C/OL]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016/6/27-2016/6/30, Las Vegas, NV, USA. IEEE, 2016. https://doi.org/10.1109/CVPR.2016.70. DOI:10.1109/cvpr.2016.70.
[14]	LARADJI I H, ROSTAMZADEH N, PINHEIRO P O, et al. Where are the blobs: Counting by localization with point supervision[M/OL]//Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018: 560-576. https://doi.org/10.1007/978-3-030-01216-8_34. DOI:10.1007/978-3-030-01216-8_34.
[15]	GOLDMAN E, HERZIG R, EISENSCHTAT A, et al. Precise detection in densely packed scenes[C/OL]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019/6/15-2019/6/20, Long Beach, CA, USA. IEEE, 2019. https://doi.org/10.1109/cvpr.2019.00537. DOI:10.1109/cvpr.2019.00537.
[16]	LIU L, CHEN J, WU H, et al. Cross-modal collaborative representation learning and a large-scale RGBT benchmark for crowd counting[C/OL]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021/6/20-2021/6/25, Nashville, TN, USA. IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00479. DOI:10.1109/cvpr46437.2021.00479.
[17]	GOLDMAN E, HERZIG R, EISENSCHTAT A, et al. Precise detection in densely packed scenes[Z/OL]//arXiv [cs.CV]. (2019-04-01). http://arxiv.org/abs/1904.00853.
[18]	GAO J, ZHAO L, LI X. NWPU-MOC: A benchmark for fine-grained multicategory object counting in aerial images[J/OL]. IEEE transactions on geoscience and remote sensing: a publication of the IEEE Geoscience and Remote Sensing Society, 2024, 62: 1-14. http://dx.doi.org/10.1109/tgrs.2024.3356492. DOI:10.1109/tgrs.2024.3356492.
[19]	LIU P, LEI S, LI H C. Mamba-MOC: A multicategory remote object counting via state space model[Z/OL]//arXiv [cs.CV]. (2025-01-11). http://arxiv.org/abs/2501.06697.
[20]	TING P, LIN J, YU W, et al. TFCounter:Polishing gems for training-free object counting[Z/OL]//arXiv [cs.CV]. (2024-03-12). http://arxiv.org/abs/2405.02301.
[21]	CHAN A B, LIANG Z S J, VASCONCELOS N. Privacy preserving crowd monitoring: Counting people without people models or tracking[C/OL]//2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008/6/23-2008/6/28, Anchorage, AK, USA. IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587569. DOI:10.1109/cvpr.2008.4587569.
[22]	CHEN K, LOY C C, GONG S, et al. Feature mining for localised crowd counting[C/OL]//Procedings of the British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.21. DOI:10.5244/C.26.21.
[23]	ZHANG C, LI H, WANG X, et al. Cross-scene crowd counting via deep convolutional neural networks[C/OL]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015/6/7-2015/6/12, Boston, MA, USA. IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298684. DOI:10.1109/cvpr.2015.7298684.
[24]	XIA Y, HE Y, PENG S, et al. CFFNet: Coordinated feature fusion network for crowd counting[J/OL]. Image and vision computing, 2021, 112(104242): 104242. http://dx.doi.org/10.1016/j.imavis.2021.104242. DOI:10.1016/j.imavis.2021.104242.
[25]	PENG D, SUN Z, CHEN Z, et al. Detecting heads using feature refine net and cascaded multi-scale architecture[C/OL]//2018 24th International Conference on Pattern Recognition (ICPR), 2018/8/20-2018/8/24, Beijing. IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8545068. DOI:10.1109/icpr.2018.8545068.
[26]	LIAN D, LI J, ZHENG J, et al. Density map regression guided detection network for RGB-D crowd counting and localization[C/OL]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019/6/15-2019/6/20, Long Beach, CA, USA. IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00192. DOI:10.1109/cvpr.2019.00192.
[27]	SINDAGI V, YASARLA R, PATEL V. Pushing the frontiers of unconstrained crowd counting: New dataset and benchmark method[C/OL]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019/10/27-2019/11/2, Seoul, Korea (South). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00131. DOI:10.1109/iccv.2019.00131.
[28]	ABOUSAMRA S, HOAI M, SAMARAS D, et al. Localization in the crowd with topological constraints[J/OL]. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2021, 35(2): 872-881. http://dx.doi.org/10.1609/aaai.v35i2.16170. DOI:10.1609/aaai.v35i2.16170.
[29]	WANG Q, GAO J, LIN W, et al. NWPU-crowd: A large-scale benchmark for crowd counting and localization[J/OL]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(6): 2141-2149. http://dx.doi.org/10.1109/TPAMI.2020.3013269. DOI:10.1109/TPAMI.2020.3013269.
[30]	YANG S, GUO W, REN Y. CrowdFormer: An overlap patching vision transformer for Top-Down Crowd counting[C/OL]//Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022/7/23-2022/7/29, Vienna, Austria. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/215. DOI:10.24963/ijcai.2022/215.
[31]	LIANG D, XIE J, ZOU Z, et al. CrowdCLIP: Unsupervised crowd counting via vision-language model[C/OL]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023/6/17-2023/6/24, Vancouver, BC, Canada. IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00283. DOI:10.1109/cvpr52729.2023.00283.
[32]	LIU C, LU H, CAO Z, et al. Point-query quadtree for crowd counting, localization, and more[Z/OL]//arXiv [cs.CV]. (2023-08-26). http://arxiv.org/abs/2308.13814.
[33]	MUNDHENK T N, KONJEVOD G, SAKLA W A, et al. A large contextual dataset for classification, detection and counting of cars with deep learning[Z/OL]//arXiv [cs.CV]. (2016-09-14). http://arxiv.org/abs/1609.04453.
[34]	MUNDHENK T N, KONJEVOD G, SAKLA W A, et al. A large contextual dataset for classification, detection and counting of cars with deep learning[M/OL]//Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 785-800. https://doi.org/10.1007/978-3-319-46487-9_48. DOI:10.1007/978-3-319-46487-9_48.
[35]	ARTETA C, LEMPITSKY V, NOBLE J A, et al. Learning to detect cells using non-overlapping extremal regions[J/OL]. Medical image computing and computer-assisted intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012, 15(Pt 1): 348-356. https://doi.org/10.1007/978-3-642-33415-3_43. DOI:10.1007/978-3-642-33415-3_43.
[36]	ARTETA C, LEMPITSKY V, NOBLE J A, et al. Detecting overlapping instances in microscopy images using extremal region trees[J/OL]. Medical image analysis, 2016, 27: 3-16. http://dx.doi.org/10.1016/j.media.2015.03.002. DOI:10.1016/j.media.2015.03.002.
[37]	XIE W, NOBLE J A, ZISSERMAN A. Microscopy cell counting and detection with fully convolutional regression networks[J/OL]. Computer methods in biomechanics and biomedical engineering. Imaging & visualization, 2018, 6(3): 283-292. https://doi.org/10.1080/21681163.2016.1149104. DOI:10.1080/21681163.2016.1149104.
[38]	GUO Y, WU G, STEIN J, et al. SAU-net: A universal Deep Network for cell counting[J/OL]. ACM-BCB ... ...: the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2019, 2019: 299-306. https://doi.org/10.1145/3307339.3342153. DOI:10.1145/3307339.3342153.
[39]	TYAGI A K, MOHAPATRA C, DAS P, et al. DeGPR: Deep guided posterior regularization for multi-class cell detection and counting[C/OL]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023/6/17-2023/6/24, Vancouver, BC, Canada. IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.02290. DOI:10.1109/cvpr52729.2023.02290.
[40]	WANG Z. Cross-domain microscopy cell counting by disentangled transfer learning[M/OL]//Trustworthy Machine Learning for Healthcare. Cham: Springer Nature Switzerland, 2023: 93-105. http://dx.doi.org/10.1007/978-3-031-39539-0_9. DOI:10.1007/978-3-031-39539-0_9.
[41]	HE S, MINN K T, SOLNICA-KREZEL L, et al. Deeply-supervised density regression for automatic cell counting in microscopy images[J/OL]. Medical image analysis, 2021, 68(101892): 101892. https://doi.org/10.1016/j.media.2020.101892. DOI:10.1016/j.media.2020.101892.
[42]	Dense Buddha head object detection and counting YOLOv8 network based on multiscale attention and data augmentation fusion[M].
[43]	ROTZAL Z. Remote Sensing Multi-class Object Counting: A YOLO Approach[J/OL]. https://cs231n.stanford.edu/2024/papers/remote-sensing-multi-class-object-counting-a-yolo-approach.pdf.
[44]	LI J, ZHU Z, LIU H, et al. Strawberry R-CNN: Recognition and counting model of strawberry based on improved faster R-CNN[J/OL]. Ecological informatics, 2023, 77(102210): 102210. http://dx.doi.org/10.1016/j.ecoinf.2023.102210. DOI:10.1016/j.ecoinf.2023.102210.
[45]	CHEN L, ZHANG Z, PENG L. Fast single shot multibox detector and its application on vehicle counting system[J/OL]. IET intelligent transport systems, 2018, 12(10): 1406-1413. http://dx.doi.org/10.1049/iet-its.2018.5005. DOI:10.1049/iet-its.2018.5005.
[46]	MADEC S, JIN X, LU H, et al. Ear density estimation from high resolution RGB imagery using deep learning technique[J/OL]. Agricultural and forest meteorology, 2019, 264: 225-234. http://dx.doi.org/10.1016/j.agrformet.2018.10.013. DOI:10.1016/j.agrformet.2018.10.013.
[47]	XUE Y, RAY N, HUGH J, et al. Cell counting by regression using convolutional neural network[M/OL]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016: 274-290. http://dx.doi.org/10.1007/978-3-319-46604-0_20. DOI:10.1007/978-3-319-46604-0_20.
[48]	Automatic vehicle counting area creation based on vehicle Deep Learning detection and DBSCAN[M].
[49]	ZABAWA L, KICHERER A, KLINGBEIL L, et al. Counting of grapevine berries in images via semantic segmentation using convolutional neural networks[Z/OL]//arXiv [cs.CV]. (2020-04-29). http://arxiv.org/abs/2004.14010.
[50]	ARTETA C, LEMPITSKY V, ZISSERMAN A. Counting in the Wild[M/OL]//Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 483-498. http://dx.doi.org/10.1007/978-3-319-46478-7_30. DOI:10.1007/978-3-319-46478-7_30.
[51]	WANG B, LIU H, SAMARAS D, et al. Distribution Matching for crowd counting[Z/OL]//arXiv [cs.CV]. (2020-09-28). http://arxiv.org/abs/2009.13077.
[52]	KHAN A, GOULD S, SALZMANN M. Deep convolutional neural networks for human embryonic cell counting[M/OL]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016: 339-348. http://dx.doi.org/10.1007/978-3-319-46604-0_25. DOI:10.1007/978-3-319-46604-0_25.
[53]	STAHL T, PINTEA S L, VAN GEMERT J C. Divide and count: Generic object counting by image divisions[J/OL]. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 2018, 28(2): 1035-1044. https://ieeexplore.ieee.org/abstract/document/8488575/. DOI:10.1109/TIP.2018.2875353.
[54]	WAWERLA J, MARSHALL S, MORI G, et al. BearCam: automated wildlife monitoring at the arctic circle[J/OL]. Machine vision and applications, 2009, 20(5): 303-317. http://dx.doi.org/10.1007/s00138-008-0128-0. DOI:10.1007/s00138-008-0128-0.
[55]	TAMERSOY B, AGGARWAL J K. Counting vehicles in highway surveillance videos[C/OL]//2010 20th International Conference on Pattern Recognition, 2010/8/23-2010/8/26, Istanbul, Turkey. IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.886. DOI:10.1109/icpr.2010.886.
[56]	LU H, CAO Z, XIAO Y, et al. TasselNet: counting maize tassels in the wild via local counts regression network[J/OL]. Plant methods, 2017, 13: 79. http://dx.doi.org/10.1186/s13007-017-0224-0. DOI:10.1186/s13007-017-0224-0.
[57]	RABAUD V, BELONGIE S. Counting crowded moving objects[C/OL]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR’06). IEEE, 2006. http://dx.doi.org/10.1109/cvpr.2006.92. DOI:10.1109/cvpr.2006.92.
[58]	ZHANG L, LI Y, NEVATIA R. Global data association for multi-object tracking using network flows[C/OL]//2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008/6/23-2008/6/28, Anchorage, AK, USA. IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587584. DOI:10.1109/cvpr.2008.4587584.
[59]	IDREES H, SALEEMI I, SEIBERT C, et al. Multi-source multi-scale counting in extremely dense crowd images[C/OL]//2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013/6/23-2013/6/28, Portland, OR, USA. IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.329. DOI:10.1109/cvpr.2013.329.
[60]	PHAM V Q, KOZAKAYA T, YAMAGUCHI O, et al. COUNT forest: CO-voting uncertain number of targets using random forest for crowd density estimation[C/OL]//2015 IEEE International Conference on Computer Vision (ICCV), 2015/12/7-2015/12/13, Santiago, Chile. IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.372. DOI:10.1109/iccv.2015.372.
[61]	IDREES H, TAYYAB M, ATHREY K, et al. Composition loss for counting, density map estimation and localization in dense crowds[M/OL]//Computer Vision – ECCV 2018. Cham: Springer International Publishing, 2018: 544-559. http://dx.doi.org/10.1007/978-3-030-01216-8_33. DOI:10.1007/978-3-030-01216-8_33.
[62]	MA Z, WEI X, HONG X, et al. Bayesian loss for crowd count estimation with point supervision[C/OL]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019/10/27-2019/11/2, Seoul, Korea (South). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00624. DOI:10.1109/iccv.2019.00624.
[63]	SHARMA R M. Single-image crowd counting using multi-column neural network[J/OL]. International journal of computer applications, 2020, 175(11): 31-35. http://dx.doi.org/10.5120/ijca2020920598. DOI:10.5120/ijca2020920598.
[64]	SHU W, WAN J, TAN K C, et al. Crowd counting in the frequency domain[C/OL]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022/6/18-2022/6/24, New Orleans, LA, USA. IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01900. DOI:10.1109/cvpr52688.2022.01900.
[65]	ZOU Z, QU X, ZHOU P, et al. Coarse to fine: Domain adaptive crowd counting via adversarial scoring network[C/OL]//Proceedings of the 29th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3475377. DOI:10.1145/3474085.3475377.
[66]	RANJAN V, SHARMA U, NGUYEN T, et al. Learning to count everything[C/OL]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021/6/20-2021/6/25, Nashville, TN, USA. IEEE, 2021. https://doi.org/10.1109/CVPR46437.2021.00340. DOI:10.1109/cvpr46437.2021.00340.
[67]	SHI M, LU H, FENG C, et al. Represent, compare, and learn: A similarity-aware framework for class-agnostic counting[C/OL]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022/6/18-2022/6/24, New Orleans, LA, USA. IEEE, 2022. https://doi.org/10.1109/CVPR52688.2022.00931. DOI:10.1109/cvpr52688.2022.00931.
[68]	YANG S D, SU H T, HSU W H, et al. Class-agnostic few-shot object counting[C/OL]//2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021/1/3-2021/1/8, Waikoloa, HI, USA. IEEE, 2021. http://dx.doi.org/10.1109/wacv48630.2021.00091. DOI:10.1109/wacv48630.2021.00091.
[69]	YOU Z, YANG K, LUO W, et al. Few-shot object counting with similarity-aware feature enhancement[C/OL]//2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023/1/2-2023/1/7, Waikoloa, HI, USA. IEEE, 2023. https://doi.org/10.1109/WACV56688.2023.00625. DOI:10.1109/wacv56688.2023.00625.
[70]	LU E, XIE W, ZISSERMAN A. Class-agnostic counting[M/OL]//Computer Vision – ACCV 2018. Cham: Springer International Publishing, 2019: 669-684. https://doi.org/10.1007/978-3-030-20893-6_42. DOI:10.1007/978-3-030-20893-6_42.
[71]	RANJAN V, NGUYEN M H. Exemplar free class agnostic counting[M/OL]//Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2023: 71-87. http://dx.doi.org/10.1007/978-3-031-26316-3_5. DOI:10.1007/978-3-031-26316-3_5.
[72]	WANG Y, YANG B, WANG X, et al. SATCount: A scale-aware transformer-based class-agnostic counting framework[J/OL]. Neural networks: the official journal of the International Neural Network Society, 2024, 172(106126): 106126. https://www.sciencedirect.com/science/article/pii/S089360802400042X. DOI:10.1016/j.neunet.2024.106126.
[73]	ZHU H, YUAN J, YANG Z, et al. FocalCount: Towards class-count imbalance in class-agnostic counting[Z/OL]//arXiv [cs.CV]. (2025-02-15). http://arxiv.org/abs/2502.10677.
[74]	HOBLEY M, PRISACARIU V. Learning to count anything: Reference-less class-agnostic counting with weak supervision[Z/OL]//arXiv [cs.CV]. (2022-05-20). https://doi.org/10.48550/arXiv.2205.10203. DOI:10.48550/ARXIV.2205.10203.
[75]	LIU C, ZHONG Y, ZISSERMAN A, et al. CounTR: Transformer-based generalised visual counting[Z/OL]//arXiv [cs.CV]. (2022-08-29). https://doi.org/10.48550/arXiv.2208.13721. DOI:10.48550/ARXIV.2208.13721.
[76]	ZHU H, YUAN J, YANG Z, et al. Zero-shot object counting with good exemplars[M/OL]//Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2025: 368-385. https://doi.org/10.1007/978-3-031-72652-1_22. DOI:10.1007/978-3-031-72652-1_22.
[77]	WANG Z, XIAO L, CAO Z, et al. Vision transformer off-the-shelf: A surprising baseline for few-shot class-agnostic counting[Z/OL]//arXiv [cs.CV]. (2023-05-07). https://doi.org/10.48550/arXiv.2305.04440. DOI:10.48550/ARXIV.2305.04440.
[78]	DANIEL ALBU R, EMILIA GORDAN C. Counting atypical metal pieces system. An eddy currents approach[C/OL]//2021 16th International Conference on Engineering of Modern Electric Systems (EMES), 2021/6/10-2021/6/11, Oradea, Romania. IEEE, 2021. http://dx.doi.org/10.1109/emes52337.2021.9484155. DOI:10.1109/emes52337.2021.9484155.
[79]	MAHESWARI S, BAGYAM M L N, VISHNUKUMAR R, et al. Sensor-based real-time monitoring system for product counting, inspection and production analysis in industrial environment[C/OL]//2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024/6/24-2024/6/28, Kamand, India. IEEE, 2024: 1-4. https://doi.org/10.1109/icccnt61001.2024.10724606. DOI:10.1109/icccnt61001.2024.10724606.
[80]	JIANG Z, ZHAI Y, KE F, et al. Learning to count arbitrary industrial manufacturing workpieces[J/OL]. IEEE transactions on industrial informatics, 2024, 20(5): 7719-7731. http://dx.doi.org/10.1109/TII.2024.3363063. DOI:10.1109/tii.2024.3363063.
[81]	Development of an Artificial Intelligence (AI) Based Visual Counting System for the Food Industry[M].
[82]	JIANG Z, LIAO J, WANG W, et al. WPCNet: Highly accurate workpiece counting network in unrestricted scenes[C/OL]//2022 4th International Conference on Applied Machine Learning (ICAML), 2022/7/23-2022/7/25, Changsha, China. IEEE, 2022. http://dx.doi.org/10.1109/icaml57167.2022.00056. DOI:10.1109/icaml57167.2022.00056.
[83]	KUMAR K, KUMAR P, KSHIRSAGAR V, et al. A real-time object counting and collecting device for industrial automation process using machine vision[J/OL]. IEEE sensors journal, 2023, 23(12): 13052-13059. http://dx.doi.org/10.1109/JSEN.2023.3267101. DOI:10.1109/jsen.2023.3267101.
[84]	XU H, CHEN G, WANG Z, et al. RGB-D-based pose estimation of workpieces with semantic segmentation and point cloud registration[J/OL]. Sensors (Basel, Switzerland), 2019, 19(8): 1873. http://dx.doi.org/10.3390/s19081873. DOI:10.3390/s19081873.
[85]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C/OL]//2017 IEEE International Conference on Computer Vision (ICCV), 2017/10/22-2017/10/29, Venice. IEEE, 2017. https://doi.org/10.1109/iccv.2017.324. DOI:10.1109/iccv.2017.324.
[86]	KIRILLOV A, MINTUN E, RAVI N, et al. Segment Anything[Z/OL]//arXiv [cs.CV]. (2023-04-05). http://arxiv.org/abs/2304.02643. DOI:10.48550/ARXIV.2304.02643.
[87]	SHI Z, SUN Y, ZHANG M. Training-free object counting with prompts[Z/OL]//arXiv [cs.CV]. (2023-06-30). https://doi.org/10.48550/arXiv.2307.00038. DOI:10.48550/ARXIV.2307.00038.
[88]	LIU S, ZENG Z, REN T, et al. Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection[Z/OL]//arXiv [cs.CV]. (2023-03-09). http://arxiv.org/abs/2303.05499. DOI:10.48550/ARXIV.2303.05499.
[89]	NGUYEN T, PHAM C, NGUYEN K, et al. Few-shot object counting and detection[Z/OL]//arXiv [cs.CV]. (2022-07-22). http://dx.doi.org/10.1007/978-3-031-20044-1_20. DOI:10.1007/978-3-031-20044-1_20.
[90]	DWIBEDI D, AYTAR Y, TOMPSON J, et al. A short note on evaluating RepNet for temporal repetition counting in videos[Z/OL]//arXiv [cs.CV]. (2024-11-13). http://arxiv.org/abs/2411.08878.
[91]	DWIBEDI D, AYTAR Y, TOMPSON J, et al. OVR: A dataset for open vocabulary temporal repetition counting in videos[Z/OL]//arXiv [cs.CV]. (2024-07-24). http://arxiv.org/abs/2407.17085.
[92]	ZHANG S, ZHAI G, CHEN K, et al. CFENet: Context-aware Feature Enhancement Network for efficient few-shot object counting[J/OL]. Image and vision computing, 2025, 154(105383): 105383. https://doi.org/10.1016/j.imavis.2024.105383. DOI:10.1016/j.imavis.2024.105383.
[93]	SU Y, WANG Y, YAO L, et al. Few-shot class-agnostic counting with occlusion augmentation and localization[C/OL]//2024 IEEE International Symposium on Circuits and Systems (ISCAS), 2024/5/19-2024/5/22, Singapore, Singapore. IEEE, 2024. https://doi.org/10.1109/iscas58744.2024.10558069. DOI:10.1109/iscas58744.2024.10558069.
[94]	CIAMPI L, AZMOUDEH A, AKBABA E E, et al. A survey on class-agnostic counting: Advancements from reference-based to open-world text-guided approaches[Z/OL]//arXiv [cs.CV]. (2025-01-31). http://arxiv.org/abs/2501.19184.
[95]	WANG Z, PAN Z, PENG Z, et al. Exploring contextual attribute density in referring expression counting[Z/OL]//arXiv [cs.CV]. (2025-03-16). http://arxiv.org/abs/2503.12460.
[96]	Single Domain Generalization for Few-Shot Counting via Universal Representation Matching[M].
[97]	PENG Z, CHAN S H G. Single domain generalization for crowd counting[Z/OL]//arXiv [cs.CV]. (2024-03-14). http://arxiv.org/abs/2403.09124.
[98]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J/OL]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149. https://doi.org/10.1109/tpami.2016.2577031. DOI:10.1109/TPAMI.2016.2577031.
[99]	LIEN C C, WU P C. A crowded object counting system with self-attention mechanism[J/OL]. Sensors (Basel, Switzerland), 2024, 24(20). http://dx.doi.org/10.3390/s24206612. DOI:10.3390/s24206612.
[100]	WANG Q, GAO J, LIN W, et al. Pixel-wise crowd understanding via synthetic data[J/OL]. International journal of computer vision, 2021, 129(1): 225-245. http://dx.doi.org/10.1007/s11263-020-01365-4. DOI:10.1007/s11263-020-01365-4.
[101]	SINDAGI V A, YASARLA R, PATEL V M. JHU-CROWD++: Large-scale crowd counting dataset and A benchmark method[J/OL]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(5): 2594-2609. http://dx.doi.org/10.1109/TPAMI.2020.3035969. DOI:10.1109/TPAMI.2020.3035969.
[102]	LI H, LIU L, YANG K, et al. Video crowd localization with multifocus Gaussian neighborhood attention and a large-scale benchmark[J/OL]. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 2022, 31: 6032-6047. http://dx.doi.org/10.1109/TIP.2022.3205210. DOI:10.1109/TIP.2022.3205210.
[103]	TANNER F, COLDER B, PULLEN C, et al. Overhead imagery research data set — an annotated data library & tools to aid in the development of computer vision algorithms[C/OL]//2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), 2009/10/14-2009/10/16, Washington, DC, USA. IEEE, 2009. http://dx.doi.org/10.1109/aipr.2009.5466304. DOI:10.1109/aipr.2009.5466304.
[104]	Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision Michael Hobley[M].
[105]	HUANG Z, DAI M, ZHANG Y, et al. Point, segment and count: A generalized framework for object counting[Z/OL]//arXiv [cs.CV]. (2023-11-21). https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Point_Segment_and_Count_A_Generalized_Framework_for_Object_Counting_CVPR_2024_paper.pdf. DOI:10.48550/ARXIV.2311.12386.
[106]	HUANG Y, RANJAN V, HOAI M. Interactive class-agnostic object counting[Z/OL]//arXiv [cs.CV]. (2023-09-11). http://arxiv.org/abs/2309.05277.
[107]	MENG Y, BRIDGE J, ZHAO Y, et al. Transportation object counting with graph-based adaptive auxiliary learning[J/OL]. IEEE transactions on intelligent transportation systems: a publication of the IEEE Intelligent Transportation Systems Council, 2023, 24(3): 3422-3437. http://dx.doi.org/10.1109/tits.2022.3226504. DOI:10.1109/tits.2022.3226504.
[108]	DAI S, LIU J, CHEUNG N M. Referring Expression Counting[C/OL]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024/6/16-2024/6/22, Seattle, WA, USA. IEEE, 2024: 16985-16995. http://dx.doi.org/10.1109/cvpr52733.2024.01607. DOI:10.1109/cvpr52733.2024.01607.
[109]	CIAMPI L, MESSINA N, PIERUCCI M, et al. Mind the prompt: A novel benchmark for prompt-based class-agnostic counting[Z/OL]//arXiv [cs.CV]. (2024-09-24). https://doi.org/10.48550/arxiv.2409.15953. DOI:10.48550/ARXIV.2409.15953.
[110]	WU Y, XU Y, XU T, et al. GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting[Z/OL]//arXiv [cs.CV]. (2024-09-18). https://doi.org/10.48550/arxiv.2409.12249. DOI:10.48550/ARXIV.2409.12249.
"""

# 使用正则表达式查找所有的DOI
doi_pattern = r'DOI:(10\.\d{4,9}/[-._;()/:A-Z0-9]+)|http[s]?://dx\.doi\.org/([^\s]+)'
dois = re.findall(doi_pattern, text)

# 提取所有匹配的DOI
dois_list = [match[0] if match[0] else match[1] for match in dois]

# 将找到的DOI列表转换为逗号分隔的字符串
dois_csv = ','.join(dois_list)

# # 统计DOI的数量
# num_dois = len(dois_list)

# print(f"Total number of DOIs found: {num_dois}")

dois_csv = dois_csv + ','

with open('doi-output.txt', 'w', encoding='utf-8') as file:
    file.truncate(0)
    file.write(dois_csv)


# 使用正则表达式查找所有的arXiv DOI
arxiv_pattern = r'http://arxiv\.org/abs/([A-Za-z0-9\.]+)'
arxiv_dois = re.findall(arxiv_pattern, text)

new_arxiv_dois = [' https://doi.org/10.48550/arXiv.'+ doi  for doi in arxiv_dois]
# print(','.join(new_arxiv_dois))
# print(len(new_arxiv_dois))
arxiv_dois_csv =  ','.join(new_arxiv_dois)
with open('doi-output.txt', 'a', encoding='utf-8') as file:
    # file.truncate(0)
    file.write(arxiv_dois_csv)